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Journal of Computational Electronics

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05-07-2023

Large-signal behavior modeling of GaN HEMTs using SSA augmented ELM algorithm

Authors: Shaowei Wang, Jincan Zhang, Shi Yang, Hao Jin, Binrui Xu, Jinchan Wang, Liwen Zhang

Published in: Journal of Computational Electronics | Issue 5/2023

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Abstract

The machine-learning algorithm is a technology that can learn from data. It can solve some problems that are difficult for humans to design and use deterministic programs. Among the available machine-learning algorithms for solving these problems, the Extreme Learning Machine (ELM) algorithm is well known because it only needs to set the number of the network hidden layer nodes and could generate a unique optimal solution. And, due to the randomness of its connection weights and thresholds, the simulated effect is random. Therefore, the selection of weights and thresholds needs to be optimized. In this paper, an enhanced ELM model is proposed to model the large-signal characteristics of GaN High Electron Mobility Transistors (HEMTs). The Sparrow Search Algorithm (SSA) is used to optimize the initial weights and thresholds of the ELM algorithm, which significantly improves the prediction ability. Moreover, the SSA algorithm has the characteristics of strong optimization ability and fast convergence speed, which greatly improves the feasibility of model training. Through comparing the training effects of the SSA-ELM model and the ELM model, it can be seen that the proposed SSA-ELM model improves the ability of the ELM model to simulate the large-signal characteristics of GaN HEMTs.

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Title: Large-signal behavior modeling of GaN HEMTs using SSA augmented ELM algorithm
Authors: Shaowei Wang
Jincan Zhang
Shi Yang
Hao Jin
Binrui Xu
Jinchan Wang
Liwen Zhang
Publication date: 05-07-2023
Publisher: Springer US
Published in: Journal of Computational Electronics / Issue 5/2023
Print ISSN: 1569-8025
Electronic ISSN: 1572-8137
DOI: https://doi.org/10.1007/s10825-023-02067-z

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Large-signal behavior modeling of GaN HEMTs using SSA augmented ELM algorithm

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Springer Professional "Wirtschaft+Technik"

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Modeling of inversion layer capacitance of III-V double gate MOSFETs using a neural network-based regression technique

Comparative study of multi-physics generated small dipoles in conducting media

Numerical simulation of a mixed-halide perovskite solar cell using doping gradient

Ab-initio simulation of dissipative transport in tunnel devices based on heterostructures of 2D materials

Recursive Green’s functions optimized for atomistic modelling of large superlattice-based devices

RSDFT-NEGF transport simulations in realistic nanoscale transistors